Chapter 18: LLM Planning

Overview

What You'll Learn: Task decomposition, ROS action mapping

Estimated Time: 7-9 hours

Core Concepts

LLM Planning enables robots to understand natural language commands and translate them into executable actions.

Example: Voice to Action Pipeline

import whisper
import openai

# Speech to text
model = whisper.load_model("base")
result = model.transcribe("audio.wav")
command = result["text"]

# LLM task planning
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{
        "role": "system",
        "content": "Decompose robot task into ROS actions"
    }, {
        "role": "user",
        "content": f"Task: {command}"
    }]
)

# Execute ROS actions
plan = response.choices[0].message.content
execute_plan(plan)

Lab

Implement voice command recognition or LLM-based task planner.

Summary

VLA bridges natural language and robotic control, enabling intuitive human-robot interaction.

Next: Chapter 19